Comparison of ANN, PSO-ANN and GA-ANN models in forecasting peak daily electricity prices, Case study: Iran Electricity Market

Document Type : Original Article

Authors

1 Assistant Professor, Water Science and Engineering Group, IKIU

2 M.S. Graduated, Water Science and Engineering Group, IKIU

Abstract

Hydro-power is one of the most important ways of providing energy in peak hours. Restructuring in the electricity industry has created rivalry among the country's electricity suppliers. In order to increase the profitability of investment and better utilization of resources, estimating the future price of electricity is of particular importance to producers. Artificial Neural Networks (ANNs), as one of the most important methods of artificial intelligence, have many uses in predicting and predicting phenomena. Recently, in order to improve the performance of the model of artificial intelligence models, their combination with optimization models has become widespread. The purpose of this study was to compare the performance of ANN, PSO-ANN and GA-ANN models in predicting the dispersed and sinusoidal data of peak daily electricity prices in Iran. The results show that the use of PSO-ANN and GA-ANN models in this case study has no superiority to the ANN model and has not improved the performance and forecast of the electricity market data.

Keywords

References

[4]  J. P. D. S. Catalao , H. M. I. Pousinho and V. M. F. Mendes, Short-term electricity prices forecasting in a competitive market by a hybrid intelligent approach, Energy Conversion and Management, 52, 2011.
[5]  N. A. Shrivastava and B. K. Panigrahi, A hybrid wavelet-ELM based short term price forecasting for electricity markets, International Journal of Electrical  Power & Energy Systems, 55, 2014.
[7]  M. Motahari and H. Mazandaranizadeh, Development of a PSO-ANN model for rainfall-runoff respons in basins, case study: Karaj Basin, Civil Engineering Journal, 2017.
[8]  D. Anderson and G. Mcneill, Artificial neural networks technology, Kaman Sciences Corporation, 258, 1992.
[9]  R. Eberchart, and J. Kennedy, Particle swarm optimization, IEEE International Conference on Neural Networks, Perth, Australia, 1995.
[10] D. D. Glodberg, Genetic algorithms in search, optimization, and machine learning, Addison Wesley publishing company Inc.: 401p.
[11] https://www.igmc.ir/Power-grid-status-report.
Journal of Renewable and New Energy
Volume 6, Issue 2 - Serial Number 12
September 2019
Pages 71-77

Files

History

  • Receive Date: 04 December 2018
  • Revise Date: 17 February 2019
  • Accept Date: 27 February 2019

Share

How to cite

Statistics